• Energy Research

AbstractTo enable sound scientific research in future energy informatics projects, it is necessary to obtain an overview of the current state of the research field to identify unaddressed gaps and challenges. Therefore, this paper aims to investigate the research trends and achievements within energy informatics in a Danish context within the last three decades. This paper reviews 207 energy informatics projects collected until the second quarter of 2022. Quantitative analysis results show that most projects have focused on applying energy informatics through energy-aware control of end-user consumption. The qualitative review finds an emphasis on data usage and end-users which aligns with the quantitative review. Furthermore, it tends to focus on specific end-users, e.g., buildings and heat pumps. Four overall recommendations are established: (I) Increased emphasis on research for sector coupling to aid in unlocking energy system flexibility, (II) project data value chain output, focusing on structuring and managing the data to make it applicable for future re-use, (III) utilizing industrial loads and incorporating an end-user perspective, (IV) inclusion of research institutions for the improved overall output of the projects through interdisciplinary solutions.

In the context of increasing environmental concerns, the iron and steel industry faces large pressure to reduce its energy consumption and carbon footprint while maintaining economic viability. This paper explores the implementation of best practice operations within foundry processes, specifically induction furnace melting, to enhance energy and cost efficiency and reduce CO2 emissions. A digital twin model is developed integrating discrete event simulation, system dynamics modeling, and symbolic regression to simulate the foundry production process and evaluate the impact of various operational practices. A large Danish foundry is used as a case study, providing data for induction furnace production incorporating various electricity market data sources. Symbolic regression models are deployed to accurately predict melt temperatures and energy requirements. Results indicate that adopting best practices can lead to significant savings - up to 21% in electricity costs and 14.2% in CO2 emissions - while improving productivity. The study also highlights a point of diminishing returns at 65% adherence to best practices due to existing production schedules. Furthermore, the study demonstrates the digital twin’s potential as a decision-support tool in optimizing industrial process operations.

Process cooling for food production is an energy-intensive industry with complex interactions and restrictions that complicate the ability to utilize energy-flexibility due to unforeseen consequences in production. Therefore, methods for assessing the potential flexibility in individual facilities to enable the active participation of process-cooling facilities in the electricity system are essential, but not yet well discussed in the literature. Therefore, this paper introduces an assessment method based on multi-method simulation and multi-objective optimization for investigating energy flexibility in process cooling, with a case study of a Danish process-cooling facility for canned-meat food production. Multi-method simulation is used in this paper: multi-agent-based simulation to investigate individual entities within the process-cooling system and the system’s behavior; discrete-event simulation to explore the entire process-cooling flow; and system dynamics to capture the thermophysical properties of the refrigeration unit and states of the refrigerated environment. A simulation library is developed, and is able to represent a generic production-flow of the canned-food process cooling. A data-driven symbolic-regression approach determines the complex logic of individual agents. Using a binary tuple-matrix for refrigeration-schedule optimization, the refrigeration-cycle operation is determined, based on weather forecasts, electricity price, and electricity CO2 emissions without violating individual room-temperature limits. The simulation results of one-week’s production in October 2020 show that 32% of energy costs can be saved and 822 kg of CO2 emissions can be reduced. The results thereby show the energy-flexibility potential in the process-cooling facilities, with the benefit of overall production cost and CO2 emissions reduction; at the same time, the production quality and throughput are not influenced.

The definition of overall district heating network performance indicators is under-investigated in the literature. This study reviews existing methods of performance estimation and develops a convenient methodology for an array of district heating networks applied to a Danish case study. Performances of the networks with state-of-art pipe transmission coefficients are compared to older traditional pipes using an effective average approach. The reported efficiencies and analysis of contributing factors show, that a single parameter is not sufficient to compare large-scale district heating systems and a multiparametric analysis must be employed. The effective average total heat transmission coefficient is evaluated based on the Technical Evaluation Factor and a multivariate regression is performed on typical sets of network parameters: pipe type, pipe series, pipe age, and operational temperature. The developed methodology is applied to testing an array of geographically independent district heating networks, pointing to possible performance bottlenecks, and discussing potential remedies.

AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

There is an increasing demand for industry-specific solutions for optimizing production processes with the transitions towards Industry 4.0. The commercial greenhouse sector relies heavily on optimal use of energy with multiple new concepts introduced in recent years e.g. vertical farming and urban agriculture. Digital twins allow utilizing the Internet of Things and big data to simulate the alternative operation strategies without compromising current operation. This paper aims to present the development of a digital twin of the commercial greenhouse production process as a part of the recently launched EUDP funded project Greenhouse Industry 4.0 in Denmark. This digital twin allows using big data and the Internet of Things to optimize the greenhouse production process and communicate with other digital twins representing essential areas in the greenhouse (climate and energy). This digital twin can estimate future states of the greenhouse by using past and real-time data inputs from databases, sensors, and spot markets. This paper also introduces a Smart Industry Architecture Model Framework for the discussion of the required data architecture of the digital twin for the greenhouse production flow which ensures a correct data architecture for the data exchange across all entities in the system.